Big Data Analytics: a basic survival requirement, or more?

Johnson Poh, Head of Data Science/Practice Lead, Big Data Analytics, DBS Bank (Singapore) lets the question hang in the air for a moment before asserting that death and taxes are not the only certainties in life. Today, he declares, there are two more certainties we have yet to acknowledge:

Limited available information for any particular situation or decision; and

Variable information that makes it hard for humans to compare, find trends and form conclusions from.

Such certainties (and they are certainties when you think about it), demonstrate the importance of data analytics within operational blueprints. The two key advantages are now well-understood:

Improving an organisation’s ability to understand and predict customer behaviour

Simultaneously optimising operations to quickly capitalise on them.

When combined, these advantages can lead both to rapid scale and competitive advantage.

Clear examples can be seen in ecommerce companies such as UBER,PropertyGuru.com and Carousell, who use data analytics and technology-centric measures to predict consumer behaviour and respond to it in their regions.

What is perhaps less understood however, is how a value-generating data science practice can be built in organisations of differing markets and maturity.

What does a successful data process look like?

The central concept of building an analytics function is that of data flow - pinpointing first where data comes from and then ultimately deciphering where it needs to go.

This means that building a robust data analytics function can be broken into three defined steps that work cohesively to support the delivery of meaningful insights to organisations:

Big Data management and processing

Analysis and advanced analytics

Software development and communication

Poh goes on to explain key components, concepts and goals of each stage, as well as the kinds of people and technologies that can fulfil each function.

1. Big Data management and processing

Typically, this stage starts with the foundational components of data analytics: the raw, the processed and the cleaned data.

The goal of this step is to set up your data infrastructure so it can effectively gather and prepare your data for analysis - and is therefore largely operational in nature, explains Poh.

It is where big data platforms, backend databases and supporting infrastructures do a lot of the heavy lifting, aided by data engineers, data scientists, data managers and programmers.

2. Analysis and advanced analytics

This is the brains of the operation, where teams of data scientists and analysts will explore and analyse the data using statistics, modelling, machine learning and exploratory analysis.

The goal is to generate knowledge and insights that are valuable to the organisation, and due to the nature of the work, projects tend to be more investigative than operational.

3. Software development and communication

It is one thing to gain insights and knowledge, Poh says, but quite another to distil it into actionable, meaningful information that your audience can understand. This final stage is therefore about creating a data product - an interface to communicate - something that can be consumed readily by those who need it.

Such visualisation of data can happen through custom application development or software that already exists. Dashboarding softwares such as QlikView and Tableau play heavily in this space, and data visualisation is predominantly resourced by software developers, UX designers and data visualisation professionals.

The work is more about the front-end communication, something Poh says is an essential final step in creating a valuable analytics function.